Escalating security concerns has led to increased installations of monitoring means such as surveillance cameras in rooms, buildings, airports, cities and etc. for monitoring purposes. However, it is labour intensive to monitor all the live events or captured videos. Although it is well recognized that monitoring these events manually would be most effective and accurate, this requires attention at all times from security personnel. The problem of keeping the attention for long periods is well known. Thus, automating the analysis of the captured monitoring material would allow the security personnel to carry out the surveillance task more effectively.
One of the demanding monitoring tasks is to detect loitering events. Detection of a loitering event is highly crucial as the loitering behaviour often is related to harmful activities such as drug-dealing activity, scene investigation for robbery and also unhealthy social problem of teenagers wasting their time in the public area.
Systems and methods for detecting loitering events typically require tracking of the object of interest to carry out the loitering detection. The accuracy of the detection of loitering events is highly dependent on the performance of the tracking methodology.
There is thus a need for improvements within this context.